We used diffusion data 1041 participants from the Human Connectome Project, processed into “tract profiles” using pyAFQ (cite) and “local connectome” features using DSI-Studio (cite).
LASSO models were run on both tract profiles and local connectome to predict a variety of cognitive outcomes.
Sparse Group LASSO (SGL) models were run on only tract profiles, to take advantage of the inherent grouping of the data.
Models were implemented in R and trained using nested cross-validation and boostrap resampling.
Explanation of/diagram of SGL here.
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| LASSO | LC | SGL | |
|---|---|---|---|
| Age | 3e-01 | 1.8e-01 | 3.1e-01 |
| Crystalized Intelligence | 7.2e-02 | 9e-02 | 7.6e-02 |
| Fluid Intelligence | 3.5e-02 | 2.7e-02 | 4.8e-02 |
| Global Intelligence | 6.8e-02 | 7.7e-02 | 8.9e-02 |
| Impulsivity | 7.1e-03 | 2.7e-02 | 8.2e-03 |
| Endurance | 1e-01 | 1e-01 | 1e-01 |
| Verbal Memory | 3.3e-03 | 1.2e-02 | 3.7e-03 |
| Reading Ability | 3.9e-02 | 8.1e-02 | 4.8e-02 |
| Attention | 3.3e-03 | 6.4e-03 | 4.4e-03 |
| Spatial Orientation | 7e-02 | 6.1e-02 | 7.2e-02 |
Table 1. Caption.
Created with (Allaire et al. 2024)